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Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems.

Authors :
Bobadilla, Jesús
Dueñas-Lerín, Jorge
Ortega, Fernando
Gutierrez, Abraham
Source :
International Journal of Interactive Multimedia & Artificial Intelligence; Jun2024, Vol. 8 Issue 6, p15-23, 9p
Publication Year :
2024

Abstract

Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a variety of accuracy and beyond accuracy quality measures, including prediction, recommendation of ordered and unordered lists, novelty, and diversity. Results show each convenient matrix factorization model attending to their simplicity, the required prediction quality, the necessary recommendation quality, the desired recommendation novelty and diversity, the need to explain recommendations, the adequacy of assigning semantic interpretations to hidden factors, the advisability of recommending to groups of users, and the need to obtain reliability values. To ensure the reproducibility of the experiments, an open framework has been used, and the implementation code is provided. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19891660
Volume :
8
Issue :
6
Database :
Complementary Index
Journal :
International Journal of Interactive Multimedia & Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
177785885
Full Text :
https://doi.org/10.9781/ijimai.2023.04.008